In addition, we further compare the Grad-CAM visualizations [1]} of SWIN and SWIN+SAFF in each class on VisDA-2017 dataset and randomly select six classes for presentation, as shown in Fig. REF . In the figure, red represents areas with high network attention, and blue-violet represents areas with low attention. As shown, SWIN+SAFF pays more attention to the discriminative features of objects, which further illustrates that SAFF can improve the discriminability of the network. Specifically, the attention of SWIN+SAFF is mainly focused on the backbone skeleton that can reflect the object category, such as the head and wing of an airplane, the two tires, and frame of a bicycle, the body of a train, and the blade of a knife, etc. In contrast, the focus of SWIN is shifted to a certain extent, such as the head of an airplane, a tire or frame of a bicycle, a small part of the body of a train, and food around a knife. Therefore, it is easier for SWIN+SAFF to infer the correct class of objects based on regions of interest. Furthermore, when more than one class of objects is present in the sample, SWIN+SAFF can better focus on the correct class with less effect of distractors. For example, in bus and motorcycle identification, SWIN focuses most of the attention on the surrounding vehicles and cyclists, while SWIN+SAFF focuses on the main body of the bus and the motorcycle, almost paying no attention to surrounding objects. Therefore, these visualization results further demonstrate that the proposed SAFF can improve the discriminability of the network.
